@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import * as tf from '../../index';
import { ALL_ENVS, describeWithFlags } from '../../jasmine_util';
import { expectArraysClose, expectArraysEqual } from '../../test_util';
describeWithFlags('nonMaxSuppression', ALL_ENVS, () => {
describe('NonMaxSuppression Basic', () => {
it('select from three clusters', async () => {
const boxes = tf.tensor2d([
0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
], [6, 4]);
const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
const maxOutputSize = 3;
const iouThreshold = 0.5;
const scoreThreshold = 0;
const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
expect(indices.shape).toEqual([3]);
expectArraysEqual(await indices.data(), [3, 0, 5]);
});
it('select from three clusters flipped coordinates', async () => {
const boxes = tf.tensor2d([
1, 1, 0, 0, 0, 0.1, 1, 1.1, 0, .9, 1, -0.1,
0, 10, 1, 11, 1, 10.1, 0, 11.1, 1, 101, 0, 100
], [6, 4]);
const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
const maxOutputSize = 3;
const iouThreshold = 0.5;
const scoreThreshold = 0;
const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
expect(indices.shape).toEqual([3]);
expectArraysEqual(await indices.data(), [3, 0, 5]);
});
it('select at most two boxes from three clusters', async () => {
const boxes = tf.tensor2d([
0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
], [6, 4]);
const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
const maxOutputSize = 2;
const iouThreshold = 0.5;
const scoreThreshold = 0;
const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
expect(indices.shape).toEqual([2]);
expectArraysEqual(await indices.data(), [3, 0]);
});
it('select at most thirty boxes from three clusters', async () => {
const boxes = tf.tensor2d([
0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
], [6, 4]);
const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
const maxOutputSize = 30;
const iouThreshold = 0.5;
const scoreThreshold = 0;
const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
expect(indices.shape).toEqual([3]);
expectArraysEqual(await indices.data(), [3, 0, 5]);
});
it('select single box', async () => {
const boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);
const scores = tf.tensor1d([0.9]);
const maxOutputSize = 3;
const iouThreshold = 0.5;
const scoreThreshold = 0;
const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
expect(indices.shape).toEqual([1]);
expectArraysEqual(await indices.data(), [0]);
});
it('select from ten identical boxes', async () => {
const numBoxes = 10;
const corners = new Array(numBoxes)
.fill(0)
.map(_ => [0, 0, 1, 1])
.reduce((arr, curr) => arr.concat(curr));
const boxes = tf.tensor2d(corners, [numBoxes, 4]);
const scores = tf.tensor1d(Array(numBoxes).fill(0.9));
const maxOutputSize = 3;
const iouThreshold = 0.5;
const scoreThreshold = 0;
const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
expect(indices.shape).toEqual([1]);
expectArraysEqual(await indices.data(), [0]);
});
it('inconsistent box and score shapes', () => {
const boxes = tf.tensor2d([
0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
], [6, 4]);
const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5]);
const maxOutputSize = 30;
const iouThreshold = 0.5;
const scoreThreshold = 0;
expect(() => tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold))
.toThrowError(/scores has incompatible shape with boxes/);
});
it('invalid iou threshold', () => {
const boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);
const scores = tf.tensor1d([0.9]);
const maxOutputSize = 3;
const iouThreshold = 1.2;
const scoreThreshold = 0;
expect(() => tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold))
.toThrowError(/iouThreshold must be in \[0, 1\]/);
});
it('empty input', async () => {
const boxes = tf.tensor2d([], [0, 4]);
const scores = tf.tensor1d([]);
const maxOutputSize = 3;
const iouThreshold = 0.5;
const scoreThreshold = 0;
const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
expect(indices.shape).toEqual([0]);
expectArraysEqual(await indices.data(), []);
});
it('accepts a tensor-like object', async () => {
const boxes = [[0, 0, 1, 1], [0, 1, 1, 2]];
const scores = [1, 2];
const indices = tf.image.nonMaxSuppression(boxes, scores, 10);
expect(indices.shape).toEqual([2]);
expect(indices.dtype).toEqual('int32');
expectArraysEqual(await indices.data(), [1, 0]);
});
it('works when inputs are not explicitly initialized on the CPU', async () => {
// This test ensures that asynchronous backends work with NMS, which
// requires inputs to reside on the CPU.
const boxes = tf.tensor2d([
0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
], [6, 4]);
const a = tf.tensor1d([0, 1, -2, -4, 4, -4]);
const b = tf.tensor1d([0.15, 0.2, 0.25, 0.5, 0.7, 1.2]);
const scores = a.div(b);
const maxOutputSize = 2;
const iouThreshold = 0.5;
const scoreThreshold = 0;
await scores.data();
const indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
expect(indices.shape).toEqual([2]);
expectArraysEqual(await indices.data(), [4, 1]);
});
});
describe('NonMaxSuppressionWithScore', () => {
it('select from three clusters with SoftNMS', async () => {
const boxes = tf.tensor2d([
0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
], [6, 4]);
const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
const maxOutputSize = 6;
const iouThreshold = 1.0;
const scoreThreshold = 0;
const softNmsSigma = 0.5;
const { selectedIndices, selectedScores } = tf.image.nonMaxSuppressionWithScore(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
expectArraysEqual(await selectedIndices.data(), [3, 0, 1, 5, 4, 2]);
expectArraysClose(await selectedScores.data(), [0.95, 0.9, 0.384, 0.3, 0.256, 0.197]);
});
});
describe('NonMaxSuppressionPadded', () => {
it('select from three clusters with pad five.', async () => {
const boxes = tf.tensor2d([
0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
], [6, 4]);
const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
const maxOutputSize = 5;
const iouThreshold = 0.5;
const scoreThreshold = 0;
const before = tf.memory().numTensors;
const { selectedIndices, validOutputs } = tf.image.nonMaxSuppressionPadded(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, true);
const after = tf.memory().numTensors;
expectArraysEqual(await selectedIndices.data(), [3, 0, 5, 0, 0]);
expectArraysEqual(await validOutputs.data(), 3);
expect(after).toEqual(before + 2);
});
it('select from three clusters with pad five and score threshold.', async () => {
const boxes = tf.tensor2d([
0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
], [6, 4]);
const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
const maxOutputSize = 6;
const iouThreshold = 0.5;
const scoreThreshold = 0.4;
const before = tf.memory().numTensors;
const { selectedIndices, validOutputs } = tf.image.nonMaxSuppressionPadded(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, true);
const after = tf.memory().numTensors;
expectArraysEqual(await selectedIndices.data(), [3, 0, 0, 0, 0, 0]);
expectArraysEqual(await validOutputs.data(), 2);
expect(after).toEqual(before + 2);
});
it('select from three clusters with no padding when pad option is false.', async () => {
const boxes = tf.tensor2d([
0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
], [6, 4]);
const scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
const maxOutputSize = 5;
const iouThreshold = 0.5;
const scoreThreshold = 0.0;
const { selectedIndices, validOutputs } = tf.image.nonMaxSuppressionPadded(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, false);
expectArraysEqual(await selectedIndices.data(), [3, 0, 5]);
expectArraysEqual(await validOutputs.data(), 3);
});
});
});
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